Single-cell metabolic fingerprints discover a cluster of circulating tumor cells with distinct metastatic potential

Metabolic reprogramming: a new option for the treatment of spinal cord injury

Spinal cord injuries impose a notably economic burden on society, mainly because of the severe after-effects they cause. Despite the ongoing development of various therapies for spinal cord injuries, their effectiveness remains unsatisfactory. However, a deeper understanding of metabolism has opened up a new therapeutic opportunity in the form of metabolic reprogramming. In this review, we explore the metabolic changes that occur during spinal cord injuries, their consequences, and the therapeutic tools available for metabolic reprogramming. Normal spinal cord metabolism is characterized by independent cellular metabolism and intercellular metabolic coupling. However, spinal cord injury results in metabolic disorders that include disturbances in glucose metabolism, lipid metabolism, and mitochondrial dysfunction. These metabolic disturbances lead to corresponding pathological changes, including the failure of axonal regeneration, the accumulation of scarring, and the activation of microglia. To rescue spinal cord injury at the metabolic level, potential metabolic reprogramming approaches have emerged, including replenishing metabolic substrates, reconstituting metabolic couplings, and targeting mitochondrial therapies to alter cell fate. The available evidence suggests that metabolic reprogramming holds great promise as a next-generation approach for the treatment of spinal cord injury. To further advance the metabolic treatment of the spinal cord injury, future efforts should focus on a deeper understanding of neurometabolism, the development of more advanced metabolomics technologies, and the design of highly effective metabolic interventions.

Circulating Tumor Cell Phenotype Detection and Epithelial-Mesenchymal Transition Tracking Based on Dual Biomarker Co-Recognition in an Integrated PDMS Chip

Circulating tumor cells (CTCs) are widely considered as a reliable and promising class of markers in the field of liquid biopsy. As CTCs undergo epithelial-mesenchymal transition (EMT), phenotype detection of heterogeneous CTCs based on EMT markers is of great significance. In this report, an integrated analytical strategy that can simultaneously capture and differentially detect epithelial- and mesenchymal-expressed CTCs in bloods of non-small cell lung cancer (NSCLS) patients is proposed. First, a commercial biomimetic polycarbonate (PCTE) microfiltration membrane is employed as the capture interface for heterogenous CTCs. Meanwhile, differential detection of the captured CTCs is realized by preparing two distinct CdTe quantum dots (QDs) with red and green emissions, attached with EpCAM and Vimentin aptamers, respectively. For combined analysis, a polydimethylsiloxane (PDMS) chip with simple structure is designed, which integrates the membrane capture and QDs-based phenotype detection of CTCs. This chip not only implements the analysis of the number of CTCs down to 2 cells mL-1, but enables EMT process tracking according to the specific signals of the two QDs. Finally, this method is successfully applied to inspect the correlations of numbers or proportions of heterogenous CTCs in 94 NSCLS patients with disease stage and whether there is distant metastasis.

Applications of single-cell technologies in drug discovery for tumor treatment

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

Adapt and shape: metabolic features within the metastatic niche

The success of disseminating cancer cells (DTCs) at specific metastatic sites is influenced by several metabolic factors. Even before DTCs arrival, metabolic conditioning from the primary tumor participates in creating a favorable premetastatic niche at distant organs. In addition, DTCs adjust their metabolism to better survive along the metastatic journey and successfully colonize their ultimate destination. However, the idea that the environment of the target organs may metabolically impact the metastatic fate is often underestimated. Here, we review the coexistence of two distinct strategies by which cancer cells shape and/or adapt to the metabolic profile of colonized tissues, ultimately creating a proper soil for their seeding and proliferation.

Rapid Screening of Biomarkers in KYSE-150 Cells Exposed to Polycyclic Aromatic Hydrocarbons via Inkjet Printing Single-Cell Mass Spectrometry

Single-cell analysis by mass spectrometry (MS) is emerging as a powerful tool that not only contributes to cellular heterogeneity but also offers an unprecedented opportunity to predict pathology onset and facilitates novel biomarker discovery. However, the development of single-cell MS analysis techniques with a focus on sample extraction, separation, and ionization methods for volume-limited samples and complexity of cellular samples are still a big challenge. In this study, we present a high-throughput approach to inkjet drop on demand printing single-cell MS for rapid screening of biomarkers of polycyclic aromatic hydrocarbon (PAH) exposure at the KYSE-150 cell, aiming to elucidate the pathogenesis of PAH-induced esophageal cancer. With an analytical bulk KYSE-150 cell throughput of up to 51 cells per minute, the method provides a new opportunity for simultaneous single-cell analysis of multiple biomarkers. We screened 930 characteristic ions from 3,683 detected peak signals and identified 91 distinctive molecules that exhibited significant differences under various concentrations of PAH exposure. These molecules have potential as clinical diagnostic biomarkers. Additionally, the current study identifies specific biomarkers that behave completely opposite in single-cell and multicell lipidomics as the concentration of PAH changes. These biomarkers potentially subdivide KYSE-150 cells into PAH-sensitive and PAH-insensitive types, providing a basis for revealing PAH toxicity and disease pathogenesis from the heterogeneity of cellular metabolism.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Exploiting the metabolic vulnerability of circulating tumour cells

Metastasis has a major part in the severity of disease and lethality of cancer. Circulating tumour cells (CTCs) represent a reservoir of metastatic precursors in circulation, most of which cannot survive due to hostile conditions in the bloodstream. Surviving cells colonise a secondary site based on a combination of physical, metabolic, and oxidative stress protection states required for that environment. Recent advances in CTC isolation methods and high-resolution 'omics technologies are revealing specific metabolic pathways that support this selection of CTCs. In this review, we discuss recent advances in our understanding of CTC biology and discoveries of adaptations in metabolic pathways during their selection. Understanding these traits and delineating mechanisms by which they confer acquired resistance or vulnerability in CTCs is crucial for developing successful prognostic and therapeutic strategies in cancer.

Single Cell mass spectrometry: Towards quantification of small molecules in individual cells

Studying cell heterogeneity can provide a deeper understanding of biological activities, but appropriate studies cannot be performed using traditional bulk analysis methods. The development of diverse single cell bioanalysis methods is in urgent need and of great significance. Mass spectrometry (MS) has been recognized as a powerful technique for bioanalysis for its high sensitivity, wide applicability, label-free detection, and capability for quantitative analysis. In this review, the general development of single cell mass spectrometry (SCMS) field is covered. First, multiple existing SCMS techniques are described and compared. Next, the development of SCMS field is discussed in a chronological order. Last, the latest quantification studies on small molecules using SCMS have been described in detail.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

A workflow for the enrichment, the identification, and the isolation of non-apoptotic single circulating tumor cells for RNA sequencing analysis

Circulating tumor cells (CTCs) are constantly shed by tumor tissue and can serve as a valuable analyte for a gene expression analysis from a liquid biopsy. However, a high proportion of CTCs can be apoptotic leading to rapid mRNA decay and challenging the analysis of their transcriptome. We established a workflow to enrich, to identify, and to isolate single CTCs including the discrimination of apoptotic and non-apoptotic CTCs for further single CTC transcriptome analysis. Viable tumor cells-we first used cells from breast cancer cell lines followed by CTCs from metastatic breast cancer patients-were enriched with the CellSearch system from diagnostic leukapheresis products, identified by immunofluorescence analysis for neoplastic markers, and isolated by micromanipulation. Then, their cDNA was generated, amplified, and sequenced. In order to exclude early apoptotic tumor cells, staining with Annexin V coupled to a fluorescent dye was used. Annexin V staining intensity was associated with decreased RNA integrity as well as lower numbers of total reads, exon reads, and detected genes in cell line cells and CTCs. A comparative RNA analysis of single cells from MDA-MB-231 and MCF7 cell lines revealed the expected differential transcriptome profiles. Enrichment and staining procedures of cell line cells that were spiked into blood had only little effect on the obtained RNA sequencing data compared to processing of naïve cells. Further, the detection of transcripts of housekeeping genes such as GAPDH was associated with a significantly higher quality of expression data from CTCs. This workflow enables the enrichment, detection, and isolation of single CTCs for individual transcriptome analyses. The discrimination of apoptotic and non-apoptotic cells allows to focus on CTCs with a high RNA integrity to ensure a successful transcriptome analysis.

Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis

Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Research progress on the multi-omics and survival status of circulating tumor cells

In the dynamic process of metastasis, circulating tumor cells (CTCs) emanate from the primary solid tumor and subsequently acquire the capacity to disengage from the basement membrane, facilitating their infiltration into the vascular system via the interstitial tissue. Given the pivotal role of CTCs in the intricate hematogenous metastasis, they have emerged as an essential resource for a deeper comprehension of cancer metastasis while also serving as a cornerstone for the development of new indicators for early cancer screening and new therapeutic targets. In the epoch of precision medicine, as CTC enrichment and separation technologies continually advance and reach full fruition, the domain of CTC research has transcended the mere straightforward detection and quantification. The rapid advancement of CTC analysis platforms has presented a compelling opportunity for in-depth exploration of CTCs within the bloodstream. Here, we provide an overview of the current status and research significance of multi-omics studies on CTCs, including genomics, transcriptomics, proteomics, and metabolomics. These studies have contributed to uncovering the unique heterogeneity of CTCs and identifying potential metastatic targets as well as specific recognition sites. We also review the impact of various states of CTCs in the bloodstream on their metastatic potential, such as clustered CTCs, interactions with other blood components, and the phenotypic states of CTCs after undergoing epithelial-mesenchymal transition (EMT). Within this context, we also discuss the therapeutic implications and potential of CTCs.

A New Era of Integration between Multiomics and Spatio-Temporal Analysis for the Translation of EMT towards Clinical Applications in Cancer

Epithelial-mesenchymal transition (EMT) is crucial to metastasis by increasing cancer cell migration and invasion. At the cellular level, EMT-related morphological and functional changes are well established. At the molecular level, critical signaling pathways able to drive EMT have been described. Yet, the translation of EMT into efficient diagnostic methods and anti-metastatic therapies is still missing. This highlights a gap in our understanding of the precise mechanisms governing EMT. Here, we discuss evidence suggesting that overcoming this limitation requires the integration of multiple omics, a hitherto neglected strategy in the EMT field. More specifically, this work summarizes results that were independently obtained through epigenomics/transcriptomics while comprehensively reviewing the achievements of proteomics in cancer research. Additionally, we prospect gains to be obtained by applying spatio-temporal multiomics in the investigation of EMT-driven metastasis. Along with the development of more sensitive technologies, the integration of currently available omics, and a look at dynamic alterations that regulate EMT at the subcellular level will lead to a deeper understanding of this process. Further, considering the significance of EMT to cancer progression, this integrative strategy may enable the development of new and improved biomarkers and therapeutics capable of increasing the survival and quality of life of cancer patients.

Targeting Translation and the Cell Cycle Inversely Affects CTC Metabolism but Not Metastasis

Melanoma brain metastasis (MBM) is significantly associated with poor prognosis and is diagnosed in 80% of patients at autopsy. Circulating tumor cells (CTCs) are "seeds" of metastasis and the smallest functional units of cancer. Our multilevel approach has previously identified a CTC RPL/RPS gene signature directly linked to MBM onset. We hypothesized that targeting ribogenesis prevents MBM/metastasis in CTC-derived xenografts. We treated parallel cohorts of MBM mice with FDA-approved protein translation inhibitor omacetaxine with or without CDK4/CDK6 inhibitor palbociclib, and monitored metastatic development and cell proliferation. Necropsies and IVIS imaging showed decreased MBM/extracranial metastasis in drug-treated mice, and RNA-Seq on mouse-blood-derived CTCs revealed downregulation of four RPL/RPS genes. However, mitochondrial stress tests and RT-qPCR showed that omacetaxine and palbociclib inversely affected glycolytic metabolism, demonstrating that dual targeting of cell translation/proliferation is critical to suppress plasticity in metastasis-competent CTCs. Equally relevant, we provide the first-ever functional metabolic characterization of patient-derived circulating neoplastic cells/CTCs.

Feasibility of mass cytometry proteomic characterisation of circulating tumour cells in head and neck squamous cell carcinoma for deep phenotyping

BACKGROUND

Circulating tumour cells (CTCs) are a potential cancer biomarker, but current methods of CTC analysis at single-cell resolution are limited. Here, we describe high-dimensional single-cell mass cytometry proteomic analysis of CTCs in HNSCC.

METHODS

Parsortix microfluidic-enriched CTCs from 14 treatment-naïve HNSCC patients were analysed by mass cytometry analysis using 41 antibodies. Immune cell lineage, epithelial-mesenchymal transition (EMT), stemness, proliferation and immune checkpoint expression was assessed alongside phosphorylation status of multiple signalling proteins. Patient-matched tumour gene expression and CTC EMT profiles were compared. Standard bulk CTC RNAseq was performed as a baseline comparator to assess mass cytometry data.

RESULTS

CTCs were detected in 13/14 patients with CTC counts of 2-24 CTCs/ml blood. Unsupervised clustering separated CTCs into epithelial, early EMT and advanced EMT groups that differed in signalling pathway activation state. Patient-specific CTC cluster patterns separated into immune checkpoint low and high groups. Patient tumour and CTC EMT profiles differed. Mass cytometry outperformed bulk RNAseq to detect CTCs and characterise cell phenotype.

DISCUSSION

We demonstrate mass cytometry allows high-plex proteomic characterisation of CTCs at single-cell resolution and identify common CTC sub-groups with potential for novel biomarker development and immune checkpoint inhibitor treatment stratification.